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@@ -262,25 +262,7 @@
langid = {english}
}
@inproceedings{choudharyMulticriteriaGroupRecommender2020,
title = {Multi-Criteria {{Group Recommender System Based}} on {{Analytical Hierarchy Process}}},
booktitle = {Smart {{Systems}} and {{IoT}}: {{Innovations}} in {{Computing}}},
author = {Choudhary, Nirmal and Bharadwaj, K. K.},
editor = {Somani, Arun K. and Shekhawat, Rajveer Singh and Mundra, Ankit and Srivastava, Sumit and Verma, Vivek Kumar},
date = {2020},
pages = {75--84},
publisher = {{Springer}},
location = {{Singapore}},
doi = {10.1007/978-981-13-8406-6_8},
abstract = {Current researches have demonstrated that the significance of Multi-Criteria Decision-Making (MCDM) methods in Group Recommender Systems (GRSs) has yet to be thoroughly discovered. Thus, we have proposed a Multi-criteria GRS (MCGRS) to provide recommendations for group of users based on multi-criteria optimization. The idea behind our approach is that, each member in a group have different opinions about each criterion and he/she would try to make the best use of multi-criteria to fulfill his/her own preference in decision-making process. Therefore, we have employed Analytical Hierarchy Process (AHP) to learn the priority of each criterion to maximize the utility for each criterion. Then, MCGRS generate the most appropriate recommendation for the group. Experiments are performed on Yahoo! Movies dataset and the results of comparative analysis of proposed MCGRS with baseline GRSs techniques clearly demonstrate the supremacy of our proposed model.},
file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\EFVTG9VE\\Choudhary_Bharadwaj_2020_Multi-criteria Group Recommender System Based on Analytical Hierarchy Process.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\GCKZA5ZN\\2018_MA_Rubinshteyn_Hybrider Recommender für die Produktkonfiguration.pdf},
isbn = {9789811384066},
keywords = {Analytical hierarchy process,Decision-making,Multi-criteria group recommender systems,Recommendation mechanism},
langid = {english},
series = {Smart {{Innovation}}, {{Systems}} and {{Technologies}}}
}
@incollection{choudharyMulticriteriaGroupRecommender2020a,
@incollection{choudharyMulticriteriaGroupRecommender2020,
title = {Multi-Criteria {{Group Recommender System Based}} on {{Analytical Hierarchy Process}}},
booktitle = {Smart {{Systems}} and {{IoT}}: {{Innovations}} in {{Computing}}},
author = {Choudhary, Nirmal and Bharadwaj, K. K.},
@@ -292,8 +274,9 @@
location = {{Singapore}},
doi = {10.1007/978-981-13-8406-6_8},
abstract = {Current researches have demonstrated that the significance of MultiCriteria Decision-Making (MCDM) methods in Group Recommender Systems (GRSs) has yet to be thoroughly discovered. Thus, we have proposed a Multi-criteria GRS (MCGRS) to provide recommendations for group of users based on multicriteria optimization. The idea behind our approach is that, each member in a group have different opinions about each criterion and he/she would try to make the best use of multi-criteria to fulfill his/her own preference in decision-making process. Therefore, we have employed Analytical Hierarchy Process (AHP) to learn the priority of each criterion to maximize the utility for each criterion. Then, MCGRS generate the most appropriate recommendation for the group. Experiments are performed on Yahoo! Movies dataset and the results of comparative analysis of proposed MCGRS with baseline GRSs techniques clearly demonstrate the supremacy of our proposed model.},
file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NN5RCJY2\\Choudhary und Bharadwaj - 2020 - Multi-criteria Group Recommender System Based on A.pdf},
file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\EFVTG9VE\\Choudhary_Bharadwaj_2020_Multi-criteria Group Recommender System Based on Analytical Hierarchy Process.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NN5RCJY2\\Choudhary und Bharadwaj - 2020 - Multi-criteria Group Recommender System Based on A.pdf},
isbn = {9789811384059 9789811384066},
keywords = {Analytical hierarchy process,Decision-making,Multi-criteria group recommender systems,Recommendation mechanism},
langid = {english}
}
@@ -1069,7 +1052,7 @@ OCLC: 935904837},
langid = {english}
}
@article{scholzConfigurationbasedRecommenderSystem2017a,
@article{scholzConfigurationbasedRecommenderSystem2017,
title = {A Configuration-Based Recommender System for Supporting e-Commerce Decisions},
author = {Scholz, Michael and Dorner, Verena and Schryen, Guido and Benlian, Alexander},
date = {2017-05},